{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline for the anomaly detection on the SKAB using LSTM-VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries importing\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# additional modules\n",
    "import sys\n",
    "sys.path.append('../utils')\n",
    "from evaluating import evaluating_change_point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# benchmark files checking\n",
    "all_files=[]\n",
    "import os\n",
    "for root, dirs, files in os.walk(\"../data/\"):\n",
    "    for file in files:\n",
    "        if file.endswith(\".csv\"):\n",
    "             all_files.append(os.path.join(root, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets with anomalies loading\n",
    "list_of_df = [pd.read_csv(file, \n",
    "                          sep=';', \n",
    "                          index_col='datetime', \n",
    "                          parse_dates=True) for file in all_files if 'anomaly-free' not in file]\n",
    "# anomaly-free df loading\n",
    "anomaly_free_df = pd.read_csv([file for file in all_files if 'anomaly-free' in file][0], \n",
    "                            sep=';', \n",
    "                            index_col='datetime', \n",
    "                            parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data description and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A number of datasets in the SkAB v1.0: 34\n",
      "\n",
      "Shape of the random dataset: (1154, 10)\n",
      "\n",
      "A number of changepoints in the SkAB v1.0: 130\n",
      "\n",
      "A number of outliers in the SkAB v1.0: 13241\n",
      "\n",
      "Head of the random dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accelerometer1RMS</th>\n",
       "      <th>Accelerometer2RMS</th>\n",
       "      <th>Current</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Thermocouple</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Volume Flow RateRMS</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>changepoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:36</th>\n",
       "      <td>0.027429</td>\n",
       "      <td>0.040353</td>\n",
       "      <td>0.770310</td>\n",
       "      <td>0.382638</td>\n",
       "      <td>71.2129</td>\n",
       "      <td>25.0827</td>\n",
       "      <td>219.789</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:37</th>\n",
       "      <td>0.027269</td>\n",
       "      <td>0.040226</td>\n",
       "      <td>1.096960</td>\n",
       "      <td>0.710565</td>\n",
       "      <td>71.4284</td>\n",
       "      <td>25.0863</td>\n",
       "      <td>233.117</td>\n",
       "      <td>32.0104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:38</th>\n",
       "      <td>0.027040</td>\n",
       "      <td>0.039773</td>\n",
       "      <td>1.140150</td>\n",
       "      <td>0.054711</td>\n",
       "      <td>71.3468</td>\n",
       "      <td>25.0874</td>\n",
       "      <td>234.745</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:39</th>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.040313</td>\n",
       "      <td>1.108680</td>\n",
       "      <td>-0.273216</td>\n",
       "      <td>71.3258</td>\n",
       "      <td>25.0897</td>\n",
       "      <td>205.254</td>\n",
       "      <td>32.0104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:41</th>\n",
       "      <td>0.026570</td>\n",
       "      <td>0.039566</td>\n",
       "      <td>0.704404</td>\n",
       "      <td>0.382638</td>\n",
       "      <td>71.2725</td>\n",
       "      <td>25.0831</td>\n",
       "      <td>212.095</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accelerometer1RMS  Accelerometer2RMS   Current  Pressure  \\\n",
       "datetime                                                                        \n",
       "2020-03-09 12:14:36           0.027429           0.040353  0.770310  0.382638   \n",
       "2020-03-09 12:14:37           0.027269           0.040226  1.096960  0.710565   \n",
       "2020-03-09 12:14:38           0.027040           0.039773  1.140150  0.054711   \n",
       "2020-03-09 12:14:39           0.027563           0.040313  1.108680 -0.273216   \n",
       "2020-03-09 12:14:41           0.026570           0.039566  0.704404  0.382638   \n",
       "\n",
       "                     Temperature  Thermocouple  Voltage  Volume Flow RateRMS  \\\n",
       "datetime                                                                       \n",
       "2020-03-09 12:14:36      71.2129       25.0827  219.789              32.0000   \n",
       "2020-03-09 12:14:37      71.4284       25.0863  233.117              32.0104   \n",
       "2020-03-09 12:14:38      71.3468       25.0874  234.745              32.0000   \n",
       "2020-03-09 12:14:39      71.3258       25.0897  205.254              32.0104   \n",
       "2020-03-09 12:14:41      71.2725       25.0831  212.095              33.0000   \n",
       "\n",
       "                     anomaly  changepoint  \n",
       "datetime                                   \n",
       "2020-03-09 12:14:36      0.0          0.0  \n",
       "2020-03-09 12:14:37      0.0          0.0  \n",
       "2020-03-09 12:14:38      0.0          0.0  \n",
       "2020-03-09 12:14:39      0.0          0.0  \n",
       "2020-03-09 12:14:41      0.0          0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset characteristics printing\n",
    "print(f'A number of datasets in the SkAB v1.0: {len(list_of_df)}\\n')\n",
    "print(f'Shape of the random dataset: {list_of_df[0].shape}\\n')\n",
    "n_cp = sum([len(df[df.changepoint==1.]) for df in list_of_df])\n",
    "n_outlier = sum([len(df[df.anomaly==1.]) for df in list_of_df])\n",
    "print(f'A number of changepoints in the SkAB v1.0: {n_cp}\\n')\n",
    "print(f'A number of outliers in the SkAB v1.0: {n_outlier}\\n')\n",
    "print(f'Head of the random dataset:')\n",
    "display(list_of_df[0].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# random dataset visualizing\n",
    "list_of_df[0].plot(figsize=(12,6))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Signals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the labels both for outlier and changepoint detection problems\n",
    "list_of_df[0].anomaly.plot(figsize=(12,3))\n",
    "list_of_df[0].changepoint.plot()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method applying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Input, LSTM, RepeatVector\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, Lambda\n",
    "from tensorflow.keras.optimizers import SGD, RMSprop, Adam\n",
    "from tensorflow.keras import losses\n",
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn import decomposition\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from scipy.signal import medfilt\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for repeatability\n",
    "def Random(seed_value):\n",
    "    # 1. Set `PYTHONHASHSEED` environment variable at a fixed value\n",
    "    import os\n",
    "    os.environ['PYTHONHASHSEED']=str(seed_value)\n",
    "\n",
    "    # 2. Set `python` built-in pseudo-random generator at a fixed value\n",
    "    import random\n",
    "    random.seed(seed_value)\n",
    "\n",
    "    # 3. Set `numpy` pseudo-random generator at a fixed value\n",
    "    import numpy as np\n",
    "    np.random.seed(seed_value)\n",
    "\n",
    "    # 4. Set `tensorflow` pseudo-random generator at a fixed value\n",
    "    import tensorflow as tf\n",
    "    tf.random.set_seed(seed_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lstm_vae(input_dim, \n",
    "    timesteps, \n",
    "    batch_size, \n",
    "    intermediate_dim, \n",
    "    latent_dim,\n",
    "    epsilon_std):\n",
    "\n",
    "    \"\"\"\n",
    "    Creates an LSTM Variational Autoencoder (VAE). Returns VAE, Encoder, Generator. \n",
    "    # Arguments\n",
    "        input_dim: int.\n",
    "        timesteps: int, input timestep dimension.\n",
    "        batch_size: int.\n",
    "        intermediate_dim: int, output shape of LSTM. \n",
    "        latent_dim: int, latent z-layer shape. \n",
    "        epsilon_std: float, z-layer sigma.\n",
    "    # References\n",
    "        - [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n",
    "        - [Generating sentences from a continuous space](https://arxiv.org/abs/1511.06349)\n",
    "    \"\"\"\n",
    "    x = Input(shape=(timesteps, input_dim,))\n",
    "\n",
    "    # LSTM encoding\n",
    "    h = LSTM(intermediate_dim)(x)\n",
    "\n",
    "    # VAE Z layer\n",
    "    z_mean = Dense(latent_dim)(h)\n",
    "    z_log_sigma = Dense(latent_dim)(h)\n",
    "    \n",
    "    def sampling(args):\n",
    "        z_mean, z_log_sigma = args\n",
    "        epsilon = K.random_normal(shape=(batch_size, latent_dim),\n",
    "                                  mean=0., stddev=epsilon_std)\n",
    "        return z_mean + z_log_sigma * epsilon\n",
    "\n",
    "    # note that \"output_shape\" isn't necessary with the TensorFlow backend\n",
    "    # so you could write `Lambda(sampling)([z_mean, z_log_sigma])`\n",
    "    z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_sigma])\n",
    "    \n",
    "    # decoded LSTM layer\n",
    "    decoder_h = LSTM(intermediate_dim, return_sequences=True)\n",
    "    decoder_mean = LSTM(input_dim, return_sequences=True)\n",
    "\n",
    "    h_decoded = RepeatVector(timesteps)(z)\n",
    "    h_decoded = decoder_h(h_decoded)\n",
    "\n",
    "    # decoded layer\n",
    "    x_decoded_mean = decoder_mean(h_decoded)\n",
    "    \n",
    "    # end-to-end autoencoder\n",
    "    vae = Model(x, x_decoded_mean)\n",
    "\n",
    "    # encoder, from inputs to latent space\n",
    "    encoder = Model(x, z_mean)\n",
    "\n",
    "    # generator, from latent space to reconstructed inputs\n",
    "    decoder_input = Input(shape=(latent_dim,))\n",
    "\n",
    "    _h_decoded = RepeatVector(timesteps)(decoder_input)\n",
    "    _h_decoded = decoder_h(_h_decoded)\n",
    "\n",
    "    _x_decoded_mean = decoder_mean(_h_decoded)\n",
    "    generator = Model(decoder_input, _x_decoded_mean)\n",
    "    \n",
    "    def vae_loss(x, x_decoded_mean):\n",
    "        mse = losses.MeanSquaredError()\n",
    "        xent_loss = mse(x, x_decoded_mean)\n",
    "        kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma))\n",
    "        loss = xent_loss + kl_loss\n",
    "        return loss\n",
    "\n",
    "    vae.compile(optimizer='rmsprop', loss=vae_loss)\n",
    "    \n",
    "    return vae, encoder, generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def arch(data):\n",
    "    Random(0)\n",
    "    \n",
    "    input_dim = data.shape[-1] # 13\n",
    "    timesteps = data.shape[1] # 3\n",
    "    BATCH_SIZE = 1\n",
    "    \n",
    "    model, enc, gen = create_lstm_vae(input_dim, \n",
    "        timesteps=timesteps, \n",
    "        batch_size=BATCH_SIZE, \n",
    "        intermediate_dim=32,\n",
    "        latent_dim=100,\n",
    "        epsilon_std=1.)\n",
    "\n",
    "    history = model.fit(\n",
    "        data,\n",
    "        data,\n",
    "        epochs=20,\n",
    "        batch_size=BATCH_SIZE,\n",
    "        validation_split=0.1,\n",
    "        verbose=0,\n",
    "        callbacks=[\n",
    "            keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=5, mode=\"min\", verbose=0)\n",
    "        ],\n",
    "    )\n",
    "    return history, model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training in the beginning of each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generated training sequences for use in the model.\n",
    "def create_sequences(values, time_steps):\n",
    "    output = []\n",
    "    for i in range(len(values) - time_steps + 1):\n",
    "        output.append(values[i : (i + time_steps)])\n",
    "    return np.stack(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1st loop: 100%|██████████| 34/34 [42:38<00:00, 75.26s/it] \n"
     ]
    }
   ],
   "source": [
    "# hyperparameters selection\n",
    "N_STEPS = 5\n",
    "Q = 0.999 # quantile for upper control limit (UCL) selection\n",
    "\n",
    "# inference\n",
    "predicted_outlier, predicted_cp = [], []\n",
    "for df in tqdm(list_of_df, desc='1st loop'):\n",
    "    X_train = df[:400].drop(['anomaly', 'changepoint'], axis=1)\n",
    "    \n",
    "    # scaler init and fitting\n",
    "    StSc = StandardScaler()\n",
    "    StSc.fit(X_train)\n",
    "    \n",
    "    # convert into input/output\n",
    "    X = create_sequences(StSc.transform(X_train), N_STEPS)\n",
    "    \n",
    "    # model defining and fitting\n",
    "    history, model = arch(X)\n",
    "    \n",
    "    # results predicting\n",
    "    residuals = pd.Series(np.sum(np.mean(np.abs(X - model.predict(X)), axis=1), axis=1))\n",
    "    UCL = residuals.quantile(Q)\n",
    "    \n",
    "    # results predicting\n",
    "    X = create_sequences(StSc.transform(df.drop(['anomaly','changepoint'], axis=1)), N_STEPS)\n",
    "    cnn_residuals = pd.Series(np.sum(np.mean(np.abs(X - model.predict(X)), axis=1), axis=1))\n",
    "    \n",
    "    # data i is an anomaly if samples [(i - timesteps + 1) to (i)] are anomalies\n",
    "    anomalous_data = cnn_residuals > (3/2 * UCL)\n",
    "    anomalous_data_indices = []\n",
    "    for data_idx in range(N_STEPS - 1, len(X) - N_STEPS + 1):\n",
    "        if np.all(anomalous_data[data_idx - N_STEPS + 1 : data_idx]):\n",
    "            anomalous_data_indices.append(data_idx)\n",
    "    \n",
    "    prediction = pd.Series(data=0, index=df.index)\n",
    "    prediction.iloc[anomalous_data_indices] = 1\n",
    "    \n",
    "    # predicted outliers saving\n",
    "    predicted_outlier.append(prediction)\n",
    "    \n",
    "    # predicted CPs saving\n",
    "    prediction_cp = abs(prediction.diff())\n",
    "    prediction_cp[0] = prediction[0]\n",
    "    predicted_cp.append(prediction_cp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true outlier indices selection\n",
    "true_outlier = [df.anomaly for df in list_of_df]\n",
    "\n",
    "predicted_outlier[0].plot(figsize=(12, 3), label='predictions', marker='o', markersize=5)\n",
    "true_outlier[0].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true changepoint indices selection\n",
    "true_cp = [df.changepoint for df in list_of_df]\n",
    "\n",
    "predicted_cp[0].plot(figsize=(12, 3), label='predictions', marker='o', markersize=5)\n",
    "true_cp[0].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Alarm Rate 9.13 %\n",
      "Missing Alarm Rate 55.03 %\n",
      "F1 metric 0.56\n"
     ]
    }
   ],
   "source": [
    "# binary classification metrics calculation\n",
    "binary = evaluating_change_point(true_outlier, predicted_outlier, metric='binary', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average delay 0 days 00:00:07.407407407\n",
      "A number of missed CPs = 103\n"
     ]
    }
   ],
   "source": [
    "# average detection delay metric calculation\n",
    "add = evaluating_change_point(true_cp, predicted_cp, metric='average_delay', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Standart  -  19.17\n",
      "LowFP  -  15.39\n",
      "LowFN  -  20.98\n"
     ]
    }
   ],
   "source": [
    "# nab metric calculation\n",
    "nab = evaluating_change_point(true_cp, predicted_cp, metric='nab', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Additional] localization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
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 },
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